We seek to apply the nonlinear modeling methodology of ``principal dynamic modes'' (PDM) developed in Core Project \#1 to a neural sensory system (insect mechanoreceptor). The fundamental question is the identification of the biophysical mechanisms that subserve mechanotransduction and the precise quantitative description of this process. The experiments (funded from other sources) employ broadband random stimuli and record the sequence of generated action potentials (spike trains). Previous studies (including kernel analysis) have advanced considerably our understanding of this physiological process but have not yielded yet a satisfactory predictive model of the spike encoding performed by the mechanoreceptor. Results from the application of the PDM method have shown excellent predictive ability of the resulting model, far exceeding the capabilities of previous models. Two principal dynamic modes have been identified in these preliminary studies, corresponding to two biophysical mechanisms. This general approach can find application to all neural systems that generate action potentials and, therefore, offers a powerful tool in a diverse and increasingly important field of biological sciences. The approach is currently tested on a different insect mechanoreceptor, with initial results suggesting again two PDM corresponding to two similar biophysical mechanisms. The stochasticity of this biological system is becoming now the focus of our study, as a paradigm for developing a methodology for studying stochastic biological systems.